Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Deconvolution01:20

Deconvolution

543
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
543

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Fragmentation Analysis of the MYC Enhancer in Cell-Free DNA by qPCR for Early Detection of Hepatocellular Carcinoma.

Hepatology research : the official journal of the Japan Society of Hepatology·2026
Same author

Consistency versus variability: Contrasting microbial ecological strategies of anaerobic digestion and activated sludge systems in full-scale Baijiu distillery wastewater treatment plants.

Water research·2026
Same author

gSV: a general structural variant detector using the third-generation sequencing data.

Briefings in bioinformatics·2026
Same author

Spatially differentiated conductive materials enable synergistic optimization of methane production and ARGs suppression in two-phase anaerobic treatment of antibiotic wastewater.

Water research·2026
Same author

Trait-Based Optimization of Plant Density in Drip-Fertigated Wheat: Yield Formation and Nitrogen-Radiation-Water Use Efficiency Responses of Varieties Contrasting in Individual Spike Productivity.

Plants (Basel, Switzerland)·2026
Same author

LSTM-based prediction and early warning of nitrogen removal in a partial denitrification-anammox bioreactor for municipal wastewater.

Journal of environmental management·2026
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

基于增强双编码器的图像细分网络.

Depeng Wang1, Yibo Sun1, Hong Chen1

  • 1China Mobile Research Institute, Business Research Institute, Beijing, China.

Scientific reports
|October 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了EDE-Net,这是一个混合深度学习模型,结合了卷积神经网络 (CNN) 和变压器,以增强医疗图像细分. EDE-Net有效地捕获了当地细节和全球背景,优于对基准数据集的现有方法.

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

相关实验视频

Last Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 卷积神经网络 (CNN) 在捕捉全球背景方面面临局限性,而变形机则在扩展性和局部特征提取方面扎.
  • 集成CNN和变压器的混合网络架构为利用两者的优势提供了一个有希望的方向.
  • 医疗图像细分需要精确地划分局部细节,并了解全球背景,以便准确诊断.

研究的目的:

  • 为改善医疗图像细分提出一个增强的双编码器网络 (EDE-Net).
  • 整合卷积内核和金字塔变压器结构,以进行全面的特征提取.
  • 开发一个高效的功能融合模块,以平衡本地和全球信息.

主要方法:

  • 拟议的EDE-Net在编码器中使用并行卷积内核和金字塔变压器结构来进行特征提取.
  • 引入了一种新的基于阶段的代特征融合 (PIFF) 模块,在每个下采样阶段将本地细节和全球特征融合在一起.
  • PIFF模块为本地和全球特征分配不同的权重,以增强前景像素分类和损伤边缘划分.

主要成果:

  • 在Glass和MoNuSeg数据集上的医疗图像细分任务中,EDE-Net表现出卓越的性能.
  • 混合方法有效地捕获了本地图像细节和全球语义信息.
  • PIFF模块显著提高了网络界定细损伤边缘的能力.

结论:

  • 集成卷积和金字塔变压器的EDE-Net为医疗图像细分提供了强大的解决方案.
  • 拟议的PIFF模块增强了特征融合,导致更准确的细分结果.
  • EDE-Net的性能优于现有的基于CNN和基于变压器的方法,突出了其临床应用的潜力.